merge() function to join two datasets.quantile().For this lab we will be, again, dealing with the meteorological dataset downloaded from the NOAA, the met. In this case, we will use data.table to answer some questions regarding the met dataset, while at the same time practice your Git+GitHub skills for this project.
This markdown document should be rendered using github_document document.
Go to your documents (or wherever you are planning to store the data) in your computer, and create a folder for this project, for example, “PM566-labs”
In that folder, save this template as “README.Rmd”. This will be the markdown file where all the magic will happen.
Go to your GitHub account and create a new repository, hopefully of the same name that this folder has, i.e., “PM566-labs”.
Initialize the Git project, add the “README.Rmd” file, and make your first commit.
Add the repo you just created on GitHub.com to the list of remotes, and push your commit to origin while setting the upstream.
Most of the steps can be done using command line:
# Step 1
cd ~/Documents
mkdir PM566-labs
cd PM566-labs
# Step 2
wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd
mv 05-lab.Rmd README.md
# Step 3
# Happens on github
# Step 4
git init
git add README.Rmd
git commit -m "First commit"
# Step 5
git remote add origin git@github.com:[username]/PM566-labs
git push -u origin masterYou can also complete the steps in R (replace with your paths/username when needed)
# Step 1
setwd("~/Documents")
dir.create("PM566-labs")
setwd("PM566-labs")
# Step 2
download.file(
"https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd",
destfile = "README.Rmd"
)
# Step 3: Happens on Github
# Step 4
system("git init && git add README.Rmd")
system('git commit -m "First commit"')
# Step 5
system("git remote add origin git@github.com:[username]/PM566-labs")
system("git push -u origin master")Once you are done setting up the project, you can now start working with the MET data.
Load the data.table (and the dtplyr and dplyr packages if you plan to work with those).
library(data.table)
library(dtplyr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:data.table':
between, first, lastThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionLoad the met data from https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz, and also the station data. For the later, you can use the code we used during lecture to pre-process the stations data:
# Lead met data
if (!file.exists("met_all.gz"))
download.file(
url = "https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz",
destfile = "met_all.gz",
method = "libcurl",
timeout = 60
)
met <- data.table::fread("met_all.gz")# Download the data
stations <- fread("ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-history.csv")
stations[, USAF := as.integer(USAF)]Warning in eval(jsub, SDenv, parent.frame()): NAs introduced by coercion# Dealing with NAs and 999999
stations[, USAF := fifelse(USAF == 999999, NA_integer_, USAF)]
stations[, CTRY := fifelse(CTRY == "", NA_character_, CTRY)]
stations[, STATE := fifelse(STATE == "", NA_character_, STATE)]
# Selecting the three relevant columns, and keeping unique records
stations <- unique(stations[, list(USAF, CTRY, STATE)])
# Dropping NAs
stations <- stations[!is.na(USAF)]
# Removing duplicates
stations[, n := 1:.N, by = .(USAF)]
stations <- stations[n == 1,][, n := NULL]Merge the data as we did during the lecture.
# Convert the USAF column to integer
stations$USAF <- as.integer(stations$USAF)
# Check for and delete duplicates in stations
stations[, n := 1:.N, by = .(USAF)]
stations <- stations[n == 1,][, n := NULL]
# Merge the data
dat <- merge(
# Data
x = met,
y = stations,
# List of variables to match
by.x = "USAFID",
by.y = "USAF",
# Which obs to keep?
all.x = TRUE,
all.y = FALSE
)What is the median station in terms of temperature, wind speed, and atmospheric pressure? Look for the three weather stations that best represent continental US using the quantile() function. Do these three coincide?
# Calculate medidians by station
medians_by_USAFID <- aggregate(
cbind(temp, wind.sp, atm.press) ~ USAFID,
data = dat,
FUN = median,
na.rm = TRUE
)
# Calculate median of the country
median_temp <- quantile(dat$temp, 0.5, na.rm = TRUE)
median_wind <- quantile(dat$wind.sp, 0.5, na.rm = TRUE)
median_press <- quantile(dat$atm.press, 0.5, na.rm = TRUE)
# Find the median stations
closest_temp <- medians_by_USAFID[which.min(abs(medians_by_USAFID$temp - median_temp)), ]
closest_wind <- medians_by_USAFID[which.min(abs(medians_by_USAFID$wind.sp - median_wind)), ]
closest_press <- medians_by_USAFID[which.min(abs(medians_by_USAFID$atm.press - median_press)), ]
closest_temp USAFID temp wind.sp atm.press
229 722860 23.55 1.5 1012.5closest_wind USAFID temp wind.sp atm.press
5 720333 22.8 2.1 1012.4closest_press USAFID temp wind.sp atm.press
131 722420 30.6 4.6 1014.1The median station for temp is USAFID 722860 in California, for wind speed is USAFID 720333 in California, and for atmospheric pressure is USAFID 722420 in Texas. None of these medians coincide.
Knit the document, commit your changes, and push it to GitHub. Don’t forget to add README.md to the tree, the first time you render it.
Just like the previous question, you are asked to identify what is the most representative, the median, station per state. This time, instead of looking at one variable at a time, look at the euclidean distance. If multiple stations show in the median, select the one located at the lowest latitude.
# Find the medians of each state
medians_by_state <- dat |>
group_by(STATE) |>
summarize(
median_temp = median(temp, na.rm = TRUE),
median_wind = median(wind.sp, na.rm = TRUE),
median_press = median(atm.press, na.rm = TRUE)
)
# Find the median stations
closest_usafid_by_state <- dat |>
group_by(STATE)|>
reframe(
closest_usafid_temp = USAFID[which.min(abs(temp - medians_by_state$median_temp[match(STATE, medians_by_state$STATE)]))],
closest_usafid_wind = USAFID[which.min(abs(wind.sp - medians_by_state$median_wind[match(STATE, medians_by_state$STATE)]))],
closest_usafid_press = USAFID[which.min(abs(atm.press - medians_by_state$median_press[match(STATE, medians_by_state$STATE)]))]
)
closest_usafid_by_state# A tibble: 46 × 4
STATE closest_usafid_temp closest_usafid_wind closest_usafid_press
<chr> <int> <int> <int>
1 AL 720265 720265 720361
2 AR 720175 720172 720175
3 AZ 720339 720339 722720
4 CA 690150 690150 690150
5 CO 720528 720385 722817
6 CT 725027 720545 725027
7 DE 724088 724088 724093
8 FL 720373 720373 720383
9 GA 720257 720257 722070
10 IA 720293 720293 725420
# ℹ 36 more rowsKnit the doc and save it on GitHub.
For each state, identify what is the station that is closest to the mid-point of the state. Combining these with the stations you identified in the previous question, use leaflet() to visualize all ~100 points in the same figure, applying different colors for those identified in this question.
# Calculate the midpoints for each state
state_midpoints <- dat |>
group_by(STATE) |>
summarize(
mid_lat = mean(lat, na.rm = TRUE), # Replace with your latitude column name
mid_lon = mean(lon, na.rm = TRUE), # Replace with your longitude column name
.groups = 'drop'
)
# Find the closest station to each state midpoint
closest_to_midpoint <- state_midpoints |>
rowwise() |>
mutate(
USAFID = dat$USAFID[which.min(sqrt((dat$lat - mid_lat)^2 + (dat$lon - mid_lon)^2))]
) |>
ungroup()
# Combine the closest stations for graphing
combined_stations <- closest_to_midpoint |>
left_join(dat |> select(USAFID, lat, lon), by = "USAFID")
# Graph using leaflet()
library(leaflet)
# Create a leaflet map
map <- leaflet() |>
addTiles() # Add OpenStreetMap tiles
# Add points for closest stations to midpoints
map <- map |>
addCircleMarkers(data = combined_stations,
~lon, ~lat,
color = "red", # Color for midpoints
radius = 5,
popup = ~paste("Station ID:", USAFID))
map <- map |>
addCircleMarkers(data = state_midpoints,
~mid_lon, ~mid_lat,
color = "blue", # Color for state midpoints
radius = 7,
label = ~paste("State Midpoint:", STATE),
group = "State Midpoints")
mapKnit the doc and save it on GitHub.
Using the quantile() function, generate a summary table that shows the number of states included, average temperature, wind-speed, and atmospheric pressure by the variable “average temperature level,” which you’ll need to create.
Start by computing the states’ average temperature. Use that measurement to classify them according to the following criteria:
# Averages by state
means_by_state <- dat |>
group_by(STATE) |>
summarize(
mean_temp = mean(temp, na.rm = TRUE),
mean_wind = mean(wind.sp, na.rm = TRUE),
mean_press = median(atm.press, na.rm = TRUE)
)
# Categorize by average temperature
means_by_state <- means_by_state |>
mutate(avg_temp_level = case_when(
mean_temp < 20 ~ "Low",
mean_temp >= 20 & mean_temp < 25 ~ "Mid",
mean_temp >= 25 ~ "High",
TRUE ~ NA_character_ # This handles any NA values if needed
))
# Create a summary table by number of states in each temperature category
summary_table <- means_by_state |>
group_by(avg_temp_level) |>
summarize(
number_of_states = n(),
avg_temp = mean(mean_temp, na.rm = TRUE),
avg_wind = mean(mean_wind, na.rm = TRUE),
avg_press = mean(mean_press, na.rm = TRUE),
.groups = 'drop'
)
summary_table# A tibble: 3 × 5
avg_temp_level number_of_states avg_temp avg_wind avg_press
<chr> <int> <dbl> <dbl> <dbl>
1 High 12 27.0 2.43 1014.
2 Low 11 18.7 2.55 1014.
3 Mid 25 22.6 2.39 1015.Once you are done with that, you can compute the following:
All by the levels described before.
# Categorize data by temperature
dat <- dat |>
mutate(avg_temp_level = case_when(
temp < 20 ~ "Low",
temp >= 20 & temp < 25 ~ "Mid",
temp >= 25 ~ "High",
TRUE ~ NA_character_ # This handles any NA values if needed
))
# Summarize by avg_temp_level
summary_stats <- dat |>
group_by(avg_temp_level) |>
summarize(
number_of_entries = n(),
number_of_na_entries = sum(is.na(dat)),
number_of_stations = n_distinct(STATE),
mean_temperature = mean(temp, na.rm = TRUE),
mean_wind_speed = mean(wind.sp, na.rm = TRUE),
mean_pressure = mean(atm.press, na.rm = TRUE),
.groups = 'drop'
)summary_stats# A tibble: 4 × 7
avg_temp_level number_of_entries number_of_na_entries number_of_stations
<chr> <int> <int> <int>
1 High 980808 2986380 48
2 Low 590757 2986380 48
3 Mid 745689 2986380 48
4 <NA> 60089 2986380 48
# ℹ 3 more variables: mean_temperature <dbl>, mean_wind_speed <dbl>,
# mean_pressure <dbl>Knit the document, commit your changes, and push them to GitHub. Once you’re done, you can email me the link to your repository or, preferably, link to the Lab 5 Issue in your commit message, as follows:
git commit -a -m "Finalizing lab 5 https://github.com/USCbiostats/PM566/issues/68"This will let me know which version of your repository to look at for grading purposes.